What is the reasoning behind the number of filters in the convolution layer?
From mathematical point of view you are correct as are your calculations. To catch all the patterns you need that many filters, but this is where a whole idea of a training comes in. Main objective of the training in the CNNs is to find just a few good patterns from billions possible ones. 

So the direct answer to your question is: The standard layers of 64 to 1024 filters are only able to catch a small part of (perhaps) useful patterns, yes but this is assuming no training taking place. If you conducted training on given data with given model, then 64 to 1024 filters could already extract a lot of useful patterns, perhaps more than needed.